The invention relates generally to diffusion magnetic resonance imaging (MRI).
As is known in the art, diffusion MRI provides a relatively sensitive probe of tissue microstructure. Owing to the microscopic length scale of diffusion in biological tissues, diffusion imaging can reveal histological architecture irresolvable by conventional MRI methods.
One known method of measuring neural connectivity noninvasively using a diffusion MRI technique is diffusion tensor imaging (DTI). DTI measures the molecular diffusion of water along neural pathways. Accurate reconstruction of neural connectivity patterns from DTI has been hindered, however, by the inability of DTI to resolve more than a single axon direction within each imaging voxel.
DTI measures the molecular diffusion, that is, the random thermal motion, of the endogenous water in brain tissue. The reconstruction of neural connectivity patterns from DTI is based on the phenomenon of diffusion anisotropy in nerve tissue: water molecules diffuse relatively freely along the neural fiber direction, but are hindered in the fiber-transverse direction. The hindrance of water diffusion in white matter is putatively due to the diffusion barrier presented by the cell membrane and the myelin sheath.
By measuring the direction of the diffusion anisotropy within each voxel, DTI provides an estimate of the neural fiber direction within each voxel. The resulting image represents a three-dimensional vector field image of the neural fiber orientation. The so-called tractography problem entails computationally reconstructing neural pathways from the diffusion tensor vector field. Reconstruction of neural pathways from DTI is confounded however by the limitation that DTI can only resolve a single fiber direction within each voxel. At the millimeter scale of the MR voxel, there is typically a distribution of fiber orientations within each voxel. For example, intravoxel orientational heterogeneity may arise from intersections between white matter bundles as well as the complex arrangement of fiber orientations at the cortical surface.
If there are multiple fiber orientations within a voxel, for example, due to fibers crossing or diverging within the voxel, DTI will estimate the fiber orientation to be the mean of the underlying fiber directions. The mean direction will not be representative of the true fiber directions, however. To consider an example, if one fiber points vertically and another fiber points horizontally, then the mean fiber direction will point towards the diagonal, differing from either of the underlying fiber directions. The inability to resolve multiple intravoxel fiber directions with DTI represents a substantial barrier to accurate mapping of white matter connectivity.
The inability of DTI to resolve multiple fiber directions stems from the assumption of Gaussian diffusion inherent to the tensor model. The tensor model assumes Gaussian diffusion and a Gaussian function only has a single directional maximum. Consequently, the tensor model cannot capture multidirectional diffusion. Recently, it has been shown that in brain regions containing fiber crossing the MR diffusion signal has significant multimodal structure, in clear disagreement with the tensor model.
In an effort to resolve the fiber-crossing confound in DTI, investigators have introduced more elaborate models of the diffusion process in neural tissue. However, the model-based approaches require a number of assumptions. The mixture modeling approach, for example, assumes that each fiber population contains Gaussian diffusion, and that there is no water exchange between fiber populations.
A model-independent approach would not need to assume a single diffusion direction and would therefore be able to detect possible fiber crossing structure. The diffusion function can be imaged model-independently using another diffusion MRI technique referred to generally as the Cartesian technique, an example of which is “q-space imaging”. The Cartesian technique measures the full three-dimensional diffusion function directly without any assumptions on the form of the underlying diffusion function. Thus, in contrast to DTI, it can resolve multiple fiber directions within a single voxel. One problem with the Cartesian technique, however, is that the Cartesian technique requires relatively long scanning times and fairly elaborate image post-processing.